Facilitation of radio access network intelligent controller resource preservation framework for 5g or other next generation network

ABSTRACT

A framework for dynamic network resource allocation and energy saving based on the real-time environment, radio network information, and machine learning (ML) can be utilized via a radio access network (RAN) intelligent controller (RIC). Real-time and predicted network utilization can facilitate resource and energy savings by leveraging the RIC platform. For example, a network information base (NIB) in the RIC platform can collects RAN and user equipment (UE) resource related information in real time and provides the abstraction of the access network in the real time. ML can predict real-time information about the UEs at time t based on data analytics and real time radio resource needs. The RIC can then instruct the network to reduce or increase resources.

RELATED APPLICATION

The subject patent application is a continuation of, and claims priorityto, U.S. Pat. Application No. 16/801,947, filed Feb. 26, 2020, andentitled “FACILITATION OF RADIO ACCESS NETWORK INTELLIGENT CONTROLLERRESOURCE PRESERVATION FRAMEWORK FOR 5G OR OTHER NEXT GENERATIONNETWORK,” the entirety of which priority application is herebyincorporated by reference herein.

TECHNICAL FIELD

This disclosure relates generally to facilitating a radio access networkintelligent controller resource preservation framework. For example,this disclosure relates to facilitating radio access network intelligentcontrols for resource and energy savings for a 5G, or other nextgeneration network, air interface.

BACKGROUND

5th generation (5G) wireless systems represent a next major phase ofmobile telecommunications standards beyond the currenttelecommunications standards of 4^(th) generation (4G). Rather thanfaster peak Internet connection speeds, 5G planning aims at highercapacity than current 4G, allowing a higher number of mobile broadbandusers per area unit, and allowing consumption of higher or unlimiteddata quantities. This would enable a large portion of the population tostream high-definition media many hours per day with their mobiledevices, when out of reach of wireless fidelity hotspots. 5G researchand development also aims at improved support of machine-to-machinecommunication, also known as the Internet of things, aiming at lowercost, lower battery consumption, and lower latency than 4G equipment.

The above-described background relating to a radio access networkintelligent controller resource preservation framework is merelyintended to provide a contextual overview of some current issues, and isnot intended to be exhaustive. Other contextual information may becomefurther apparent upon review of the following detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting and non-exhaustive embodiments of the subject disclosureare described with reference to the following figures, wherein likereference numerals refer to like parts throughout the various viewsunless otherwise specified.

FIG. 1 illustrates an example wireless communication system in which anetwork node device (e.g., network node) and user equipment (UE) canimplement various aspects and embodiments of the subject disclosure.

FIG. 2 illustrates an example schematic system block diagram of mobileedge computing platform according to one or more embodiments.

FIG. 3 illustrates an example schematic system block diagram of anexample schematic system block diagram of a resource preservationframework according to one or more embodiments according to one or moreembodiments.

FIG. 4 illustrates an example schematic system block diagram of flowdiagram for a resource preservation framework according to one or moreembodiments.

FIG. 5 illustrates an example schematic system block diagram of a closedloop control system according to one or more embodiments.

FIG. 6 illustrates an example flow diagram for a method for radio accessnetwork intelligent controller for a resource preservation framework fora 5G network according to one or more embodiments.

FIG. 7 illustrates an example flow diagram for a system for radio accessnetwork intelligent controller for a resource preservation framework fora 5G network according to one or more embodiments.

FIG. 8 illustrates an example flow diagram for a machine-readable mediumfor radio access network intelligent controller for a resourcepreservation framework for a 5G network according to one or moreembodiments.

FIG. 9 illustrates an example block diagram of an example mobile handsetoperable to engage in a system architecture that facilitates securewireless communication according to one or more embodiments describedherein.

FIG. 10 illustrates an example block diagram of an example computeroperable to engage in a system architecture that facilitates securewireless communication according to one or more embodiments describedherein.

DETAILED DESCRIPTION

In the following description, numerous specific details are set forth toprovide a thorough understanding of various embodiments. One skilled inthe relevant art will recognize, however, that the techniques describedherein can be practiced without one or more of the specific details, orwith other methods, components, materials, etc. In other instances,well-known structures, materials, or operations are not shown ordescribed in detail to avoid obscuring certain aspects.

Reference throughout this specification to “one embodiment,” or “anembodiment,” means that a particular feature, structure, orcharacteristic described in connection with the embodiment is includedin at least one embodiment. Thus, the appearances of the phrase “in oneembodiment,” “in one aspect,” or “in an embodiment,” in various placesthroughout this specification are not necessarily all referring to thesame embodiment. Furthermore, the particular features, structures, orcharacteristics may be combined in any suitable manner in one or moreembodiments.

As utilized herein, terms “component,” “system,” “interface,” and thelike are intended to refer to a computer-related entity, hardware,software (e.g., in execution), and/or firmware. For example, a componentcan be a processor, a process running on a processor, an object, anexecutable, a program, a storage device, and/or a computer. By way ofillustration, an application running on a server and the server can be acomponent. One or more components can reside within a process, and acomponent can be localized on one computer and/or distributed betweentwo or more computers.

Further, these components can execute from various machine-readablemedia having various data structures stored thereon. The components cancommunicate via local and/or remote processes such as in accordance witha signal having one or more data packets (e.g., data from one componentinteracting with another component in a local system, distributedsystem, and/or across a network, e.g., the Internet, a local areanetwork, a wide area network, etc. with other systems via the signal).

As another example, a component can be an apparatus with specificfunctionality provided by mechanical parts operated by electric orelectronic circuitry; the electric or electronic circuitry can beoperated by a software application or a firmware application executed byone or more processors; the one or more processors can be internal orexternal to the apparatus and can execute at least a part of thesoftware or firmware application. As yet another example, a componentcan be an apparatus that provides specific functionality throughelectronic components without mechanical parts; the electroniccomponents can include one or more processors therein to executesoftware and/or firmware that confer(s), at least in part, thefunctionality of the electronic components. In an aspect, a componentcan emulate an electronic component via a virtual machine, e.g., withina cloud computing system.

The words “exemplary” and/or “demonstrative” are used herein to meanserving as an example, instance, or illustration. For the avoidance ofdoubt, the subject matter disclosed herein is not limited by suchexamples. In addition, any aspect or design described herein as“exemplary” and/or “demonstrative” is not necessarily to be construed aspreferred or advantageous over other aspects or designs, nor is it meantto preclude equivalent exemplary structures and techniques known tothose of ordinary skill in the art. Furthermore, to the extent that theterms “includes,” “has,” “contains,” and other similar words are used ineither the detailed description or the claims, such terms are intendedto be inclusive - in a manner similar to the term “comprising” as anopen transition word - without precluding any additional or otherelements.

As used herein, the term “infer” or “inference” refers generally to theprocess of reasoning about, or inferring states of, the system,environment, user, and/or intent from a set of observations as capturedvia events and/or data. Captured data and events can include user data,device data, environment data, data from sensors, sensor data,application data, implicit data, explicit data, etc. Inference can beemployed to identify a specific context or action, or can generate aprobability distribution over states of interest based on aconsideration of data and events, for example.

Inference can also refer to techniques employed for composinghigher-level events from a set of events and/or data. Such inferenceresults in the construction of new events or actions from a set ofobserved events and/or stored event data, whether the events arecorrelated in close temporal proximity, and whether the events and datacome from one or several event and data sources. Various classificationschemes and/or systems (e.g., support vector machines, neural networks,expert systems, Bayesian belief networks, fuzzy logic, and data fusionengines) can be employed in connection with performing automatic and/orinferred action in connection with the disclosed subject matter.

In addition, the disclosed subject matter can be implemented as amethod, apparatus, or article of manufacture using standard programmingand/or engineering techniques to produce software, firmware, hardware,or any combination thereof to control a computer to implement thedisclosed subject matter. The term “article of manufacture” as usedherein is intended to encompass a computer program accessible from anycomputer-readable device, machine-readable device, computer-readablecarrier, computer-readable media, or machine-readable media. Forexample, computer-readable media can include, but are not limited to, amagnetic storage device, e.g., hard disk; floppy disk; magneticstrip(s); an optical disk (e.g., compact disk (CD), a digital video disc(DVD), a Blu-ray Disc™ (BD)); a smart card; a flash memory device (e.g.,card, stick, key drive); and/or a virtual device that emulates a storagedevice and/or any of the above computer-readable media.

As an overview, various embodiments are described herein to facilitate aradio access network intelligent controller resource preservationframework for a 5G air interface or other next generation networks. Forsimplicity of explanation, the methods (or algorithms) are depicted anddescribed as a series of acts. It is to be understood and appreciatedthat the various embodiments are not limited by the acts illustratedand/or by the order of acts. For example, acts can occur in variousorders and/or concurrently, and with other acts not presented ordescribed herein. Furthermore, not all illustrated acts may be requiredto implement the methods. In addition, the methods could alternativelybe represented as a series of interrelated states via a state diagram orevents. Additionally, the methods described hereafter are capable ofbeing stored on an article of manufacture (e.g., a machine-readablestorage medium) to facilitate transporting and transferring suchmethodologies to computers. The term article of manufacture, as usedherein, is intended to encompass a computer program accessible from anycomputer-readable device, carrier, or media, including a non-transitorymachine-readable storage medium.

It should be noted that although various aspects and embodiments havebeen described herein in the context of 5G, Universal MobileTelecommunications System (UMTS), and/or Long Term Evolution (LTE), orother next generation networks, the disclosed aspects are not limited to5G, a UMTS implementation, and/or an LTE implementation as thetechniques can also be applied in 3G, 4G or LTE systems. For example,aspects or features of the disclosed embodiments can be exploited insubstantially any wireless communication technology. Such wirelesscommunication technologies can include UMTS, Code Division MultipleAccess (CDMA), Wi-Fi, Worldwide Interoperability for Microwave Access(WiMAX), General Packet Radio Service (GPRS), Enhanced GPRS, ThirdGeneration Partnership Project (3GPP), LTE, Third Generation PartnershipProject 2 (3GPP2) Ultra Mobile Broadband (UMB), High Speed Packet Access(HSPA), Evolved High Speed Packet Access (HSPA+), High-Speed DownlinkPacket Access (HSDPA), High-Speed Uplink Packet Access (HSUPA), Zigbee,or another IEEE 802.12 technology. Additionally, substantially allaspects disclosed herein can be exploited in legacy telecommunicationtechnologies.

Described herein are systems, methods, articles of manufacture, andother embodiments or implementations that can facilitate a radio accessnetwork intelligent controller resource preservation framework for a 5Gnetwork. Facilitating a radio access network intelligent controllerresource preservation framework for a 5G network can be implemented inconnection with any type of device with a connection to thecommunications network (e.g., a mobile handset, a computer, a handhelddevice, etc.) any Internet of things (IOT) device (e.g., toaster, coffeemaker, blinds, music players, speakers, etc.), and/or any connectedvehicles (cars, airplanes, space rockets, and/or other at leastpartially automated vehicles (e.g., drones)). In some embodiments thenon-limiting term user equipment (UE) is used. It can refer to any typeof wireless device that communicates with a radio network node in acellular or mobile communication system. Examples of UE are targetdevice, device to device (D2D) UE, machine type UE or UE capable ofmachine to machine (M2M) communication, PDA, Tablet, mobile terminals,smart phone, laptop embedded equipped (LEE), laptop mounted equipment(LME), USB dongles etc. Note that the terms element, elements andantenna ports can be interchangeably used but carry the same meaning inthis disclosure. The embodiments are applicable to single carrier aswell as to multicarrier (MC) or carrier aggregation (CA) operation ofthe UE. The term carrier aggregation (CA) is also called (e.g.interchangeably called) “multi-carrier system”, “multi-cell operation”,“multi-carrier operation”, “multi-carrier” transmission and/orreception.

In some embodiments the non-limiting term radio network node or simplynetwork node is used. It can refer to any type of network node thatserves UE is connected to other network nodes or network elements or anyradio node from where UE receives a signal. Examples of radio networknodes are Node B, base station (BS), multi-standard radio (MSR) nodesuch as MSR BS, eNode B, network controller, radio network controller(RNC), base station controller (BSC), relay, donor node controllingrelay, base transceiver station (BTS), access point (AP), transmissionpoints, transmission nodes, RRU, RRH, nodes in distributed antennasystem (DAS) etc.

Cloud radio access networks (RAN) can enable the implementation ofconcepts such as software-defined network (SDN) and network functionvirtualization (NFV) in 5G networks. This disclosure can facilitate ageneric channel state information framework design for a 5G network.Certain embodiments of this disclosure can comprise an SDN controllerthat can control routing of traffic within the network and between thenetwork and traffic destinations. The SDN controller can be merged withthe 5G network architecture to enable service deliveries via openapplication programming interfaces (“APIs”) and move the network coretowards an all internet protocol (“IP”), cloud based, and softwaredriven telecommunications network. The SDN controller can work with, ortake the place of policy and charging rules function (“PCRF”) networkelements so that policies such as quality of service and trafficmanagement and routing can be synchronized and managed end to end.

To meet the huge demand for data centric applications, 4G standards canbe applied 5G, also called new radio (NR) access. 5G networks cancomprise the following: data rates of several tens of megabits persecond supported for tens of thousands of users; 1 gigabit per secondcan be offered simultaneously to tens of workers on the same officefloor; several hundreds of thousands of simultaneous connections can besupported for massive sensor deployments; spectral efficiency can beenhanced compared to 4G; improved coverage; enhanced signalingefficiency; and reduced latency compared to LTE. In multicarrier systemsuch as OFDM, each subcarrier can occupy bandwidth (e.g., subcarrierspacing). If the carriers use the same bandwidth spacing, then it can beconsidered a single numerology. However, if the carriers occupydifferent bandwidth and/or spacing, then it can be considered a multiplenumerology.

A radio access network (RAN) intelligent controller (RIC) can comprisean extensible real-time micro-services (e.g., xApps) framework coupledwith operator intent policy, real time data from the network and users,and network-artificial intelligence (AI) to enable a more granular RANcontrol, provide greater flexibility, and improve RAN efficiency. TheRIC platform can support various RAN control functions ranging frombasic RAN control functionality, such as traffic steering and mobilitymanagement, to the enhanced RAN control functions. Depending on functionand latency requirements, RIC supported xApps can be centralized at anedge cloud or distributed (e.g., eNB or gNB). This disclosure proposesRIC enabled enhanced resource controls based on the real-timeenvironment information and machine learning (ML)/artificialintelligence (AI) for the real-time predictions of the network toachieve resource and energy savings.

There are many scenarios that can require network resources to bedynamic (e.g., in the train stations, stadiums, shopping malls, etc.).Currently, every part of the network from core to transport to accesscan comprise a software enabled network capability that, according todemand and supply rules, can increase or decrease the resources to theincoming or outgoing traffic in a reactive fashion. 5G+ networks can runmore intelligently and efficiently if they utilize real-time data andartificial intelligence. An SDN enabled open eco-system with highmodularity and flexibility can support an entirely new generation ofapplications. The open eco-system and platform can allow for a broadercommunity to contribute and innovate RIC as an extensible real-timemicro-services framework coupled with operator intent policy, real timedata from the network, and users and network AI/ML to enable a moregranular RAN control, provide greater flexibility, and improve RANefficiency. This disclosure provides a framework for dynamic networkresource allocation and energy savings based on the real-timeenvironment, radio network information, and ML/AI for the real-timeprediction of the network. Consequently, this can achieve resource andenergy savings by leveraging the RIC platform. The RIC can instruct thenetwork to reduce the overage resources (e.g. power, transport, etc.)when the demand is down (e.g., a train is approaching the station,increase the resources in parallel).

It should also be noted that an artificial intelligence (AI) componentcan facilitate automating one or more features in accordance with thedisclosed aspects. A memory and a processor as well as other componentscan include functionality with regard to the figures. The disclosedaspects in connection with preserving resources can employ variousAI-based schemes for carrying out various aspects thereof. For example,a process for detecting one or more trigger events, reducing an outputpower as a result of the one or more trigger events, and modifying oneor more reported measurements, and so forth, can be facilitated with anexample automatic classifier system and process. In another example, aprocess for penalizing one frequency/technology while preferring anotherfrequency/technology can be facilitated with the example automaticclassifier system and process.

An example classifier can be a function that maps an input attributevector, x = (x1, x2, x3, x4, xn), to a confidence that the input belongsto a class, that is, f(x) = confidence(class). Such classification canemploy a probabilistic and/or statistical-based analysis (e.g.,factoring into the analysis utilities and costs) to prognose or infer anaction that can be automatically performed. In the case of communicationsystems, for example, attributes can be a frequency band and atechnology and the classes can be an output power reduction value. Inanother example, the attributes can be a frequency band, a technology,and the presence of an object and the classes can be an output powerreduction value.

A support vector machine (SVM) is an example of a classifier that can beemployed. The SVM can operate by finding a hypersurface in the space ofpossible inputs, which the hypersurface attempts to split the triggeringcriteria from the non-triggering events. Intuitively, this makes theclassification correct for testing data that is near, but not identicalto training data. Other directed and undirected model classificationapproaches include, for example, naive Bayes, Bayesian networks,decision trees, neural networks, fuzzy logic models, and probabilisticclassification models providing different patterns of independence canbe employed. Classification as used herein also may be inclusive ofstatistical regression that is utilized to develop models of priority.

The disclosed aspects can employ classifiers that are explicitly trained(e.g., via a generic training data) as well as implicitly trained (e.g.,via observing mobile device usage as it relates to triggering events,observing network frequency/technology, receiving extrinsic information,and so on). For example, SVMs can be configured via a learning ortraining phase within a classifier constructor and feature selectionmodule. Thus, the classifier(s) can be used to automatically learn andperform a number of functions, including but not limited to modifying atransmit power, modifying one or more reported mobility measurements,and so forth. The criteria can include, but is not limited to,predefined values, frequency attenuation tables or other parameters,service provider preferences and/or policies, and so on.

In one embodiment, described herein is a method comprising receiving, bya wireless network device comprising a processor, state datarepresentative of a state of a group of mobile devices as the group ofmobile devices approaches the wireless network device. The method cancomprise receiving, by the wireless network device, distance datarepresentative of a distance of the group of mobile devices from thenetwork device. Additionally, the method can comprise generating, by thewireless network device, a trigger condition to trigger a resourceallocation of a network resource as a function of the state data and thedistance data. Furthermore, the method can comprise monitoring, by thewireless network device, the state data and the distance data of thegroup of mobile devices to determine whether the trigger condition hasbeen satisfied in response to the generating the trigger condition.

According to another embodiment, a system can facilitate, receivingstate data representative of a state of mobile devices as the mobiledevices approach a network device at a same rate of speed. The systemcan comprise receiving distance data representative of a distance of themobile devices from the network device. As a function of the state dataand the distance data, the system operations can comprise applying atrigger condition to trigger an action associated with applying amicroservice to the mobile devices. Additionally, in response to theapplying the trigger condition, the system operations can comprisemonitoring the state data and the distance data of the mobile devices todetermine whether the trigger condition has been satisfied.

According to yet another embodiment, described herein is amachine-readable medium that can perform the operations comprisingreceiving state data representative of a state of mobile devices as themobile devices approach a network device at a same rate of speed. Themachine-readable medium can perform the operations comprising receivingdistance data representative of a distance of the mobile devices fromthe network device. Additionally, the machine-readable medium canperform the operations comprising receiving time data representative ofa time associated with the mobile devices arriving at a locationassociated with the network device. Furthermore, as a function of thestate data, the time data, and the distance data, the machine-readablemedium can perform the operations comprising generating a triggercondition to trigger an action associated with applying a microserviceto the mobile devices.

These and other embodiments or implementations are described in moredetail below with reference to the drawings.

Referring now to FIG. 1 , illustrated is an example wirelesscommunication system 100 in accordance with various aspects andembodiments of the subject disclosure. In one or more embodiments,system 100 can comprise one or more user equipment UEs 102, 106. Thenon-limiting term user equipment can refer to any type of device thatcan communicate with a network node in a cellular or mobilecommunication system. A UE can have one or more antenna panels havingvertical and horizontal elements. Examples of a UE comprise a targetdevice, device to device (D2D) UE, machine type UE or UE capable ofmachine to machine (M2M) communications, personal digital assistant(PDA), tablet, mobile terminals, smart phone, laptop mounted equipment(LME), universal serial bus (USB) dongles enabled for mobilecommunications, a computer having mobile capabilities, a mobile devicesuch as cellular phone, a laptop having laptop embedded equipment (LEE,such as a mobile broadband adapter), a tablet computer having a mobilebroadband adapter, a wearable device, a virtual reality (VR) device, aheads-up display (HUD) device, a smart car, a machine-type communication(MTC) device, and the like. User equipment UE 102 can also comprise IOTdevices that communicate wirelessly.

In various embodiments, system 100 is or comprises a wirelesscommunication network serviced by one or more wireless communicationnetwork providers. In example embodiments, a UE 102 can becommunicatively coupled to the wireless communication network via anetwork node 104. The network node (e.g., network node device) cancommunicate with user equipment (UE), thus providing connectivitybetween the UE and the wider cellular network. The UE 102 can sendtransmission type recommendation data to the network node 104. Thetransmission type recommendation data can comprise a recommendation totransmit data via a closed loop MIMO mode and/or a rank-1 precoder mode.

A network node can have a cabinet and other protected enclosures, anantenna mast, and multiple antennas for performing various transmissionoperations (e.g., MIMO operations). Network nodes can serve severalcells, also called sectors, depending on the configuration and type ofantenna. In example embodiments, the UE 102 can send and/or receivecommunication data via a wireless link to the network node 104. Thedashed arrow lines from the network node 104 to the UE 102 representdownlink (DL) communications and the solid arrow lines from the UE 102to the network nodes 104 represents an uplink (UL) communication.

System 100 can further include one or more communication serviceprovider networks that facilitate providing wireless communicationservices to various UEs, including UE 102, via the network node 104and/or various additional network devices (not shown) included in theone or more communication service provider networks. The one or morecommunication service provider networks can include various types ofdisparate networks, including but not limited to: cellular networks,femto networks, picocell networks, microcell networks, internet protocol(IP) networks Wi-Fi service networks, broadband service network,enterprise networks, cloud based networks, and the like. For example, inat least one implementation, system 100 can be or include a large scalewireless communication network that spans various geographic areas.According to this implementation, the one or more communication serviceprovider networks can be or include the wireless communication networkand/or various additional devices and components of the wirelesscommunication network (e.g., additional network devices and cell,additional UEs, network server devices, etc.). The network node 104 canbe connected to the one or more communication service provider networksvia one or more backhaul links 108. For example, the one or morebackhaul links 108 can comprise wired link components, such as a T1/E1phone line, a digital subscriber line (DSL) (e.g., either synchronous orasynchronous), an asymmetric DSL (ADSL), an optical fiber backbone, acoaxial cable, and the like. The one or more backhaul links 108 can alsoinclude wireless link components, such as but not limited to,line-of-sight (LOS) or non-LOS links which can include terrestrialair-interfaces or deep space links (e.g., satellite communication linksfor navigation).

Wireless communication system 100 can employ various cellular systems,technologies, and modulation modes to facilitate wireless radiocommunications between devices (e.g., the UE 102 and the network node104). While example embodiments might be described for 5G new radio (NR)systems, the embodiments can be applicable to any radio accesstechnology (RAT) or multi-RAT system where the UE operates usingmultiple carriers e.g. LTE FDD/TDD, GSM/GERAN, CDMA2000 etc.

For example, system 100 can operate in accordance with global system formobile communications (GSM), universal mobile telecommunications service(UMTS), long term evolution (LTE), LTE frequency division duplexing (LTEFDD, LTE time division duplexing (TDD), high speed packet access (HSPA),code division multiple access (CDMA), wideband CDMA (WCMDA), CDMA2000,time division multiple access (TDMA), frequency division multiple access(FDMA), multi-carrier code division multiple access (MC-CDMA),single-carrier code division multiple access (SC-CDMA), single-carrierFDMA (SC-FDMA), orthogonal frequency division multiplexing (OFDM),discrete Fourier transform spread OFDM (DFT-spread OFDM) single carrierFDMA (SC-FDMA), Filter bank based multi-carrier (FBMC), zero tailDFT-spread-OFDM (ZT DFT-s-OFDM), generalized frequency divisionmultiplexing (GFDM), fixed mobile convergence (FMC), universal fixedmobile convergence (UFMC), unique word OFDM (UW-OFDM), unique wordDFT-spread OFDM (UW DFT-Spread-OFDM), cyclic prefix OFDM CP-OFDM,resource-block-filtered OFDM, Wi Fi, WLAN, WiMax, and the like. However,various features and functionalities of system 100 are particularlydescribed wherein the devices (e.g., the UEs 102 and the network device104) of system 100 are configured to communicate wireless signals usingone or more multi carrier modulation schemes, wherein data symbols canbe transmitted simultaneously over multiple frequency subcarriers (e.g.,OFDM, CP-OFDM, DFT-spread OFMD, UFMC, FMBC, etc.). The embodiments areapplicable to single carrier as well as to multicarrier (MC) or carrieraggregation (CA) operation of the UE. The term carrier aggregation (CA)is also called (e.g. interchangeably called) “multi-carrier system”,“multi-cell operation”, “multi-carrier operation”, “multi-carrier”transmission and/or reception. Note that some embodiments are alsoapplicable for Multi RAB (radio bearers) on some carriers (that is dataplus speech is simultaneously scheduled).

In various embodiments, system 100 can be configured to provide andemploy 5G wireless networking features and functionalities. 5G wirelesscommunication networks are expected to fulfill the demand ofexponentially increasing data traffic and to allow people and machinesto enjoy gigabit data rates with virtually zero latency. Compared to 4G,5G supports more diverse traffic scenarios. For example, in addition tothe various types of data communication between conventional UEs (e.g.,phones, smartphones, tablets, PCs, televisions, Internet enabledtelevisions, etc.) supported by 4G networks, 5G networks can be employedto support data communication between smart cars in association withdriverless car environments, as well as machine type communications(MTCs). Considering the drastic different communication needs of thesedifferent traffic scenarios, the ability to dynamically configurewaveform parameters based on traffic scenarios while retaining thebenefits of multi carrier modulation schemes (e.g., OFDM and relatedschemes) can provide a significant contribution to the highspeed/capacity and low latency demands of 5G networks. With waveformsthat split the bandwidth into several sub-bands, different types ofservices can be accommodated in different sub-bands with the mostsuitable waveform and numerology, leading to an improved spectrumutilization for 5G networks.

To meet the demand for data centric applications, features of proposed5G networks may comprise: increased peak bit rate (e.g., 20 Gbps),larger data volume per unit area (e.g., high system spectralefficiency - for example about 3.5 times that of spectral efficiency oflong term evolution (LTE) systems), high capacity that allows moredevice connectivity both concurrently and instantaneously, lowerbattery/power consumption (which reduces energy and consumption costs),better connectivity regardless of the geographic region in which a useris located, a larger numbers of devices, lower infrastructuraldevelopment costs, and higher reliability of the communications. Thus,5G networks may allow for: data rates of several tens of megabits persecond should be supported for tens of thousands of users, 1 gigabit persecond to be offered simultaneously to tens of workers on the sameoffice floor, for example; several hundreds of thousands of simultaneousconnections to be supported for massive sensor deployments; improvedcoverage, enhanced signaling efficiency; reduced latency compared toLTE.

The upcoming 5G access network may utilize higher frequencies (e.g., > 6GHz) to aid in increasing capacity. Currently, much of the millimeterwave (mmWave) spectrum, the band of spectrum between 30 gigahertz (Ghz)and 300 Ghz is underutilized. The millimeter waves have shorterwavelengths that range from 10 millimeters to 1 millimeter, and thesemmWave signals experience severe path loss, penetration loss, andfading. However, the shorter wavelength at mmWave frequencies alsoallows more antennas to be packed in the same physical dimension, whichallows for large-scale spatial multiplexing and highly directionalbeamforming.

Performance can be improved if both the transmitter and the receiver areequipped with multiple antennas. Multi-antenna techniques cansignificantly increase the data rates and reliability of a wirelesscommunication system. The use of multiple input multiple output (MIMO)techniques, which was introduced in the third-generation partnershipproject (3GPP) and has been in use (including with LTE), is amulti-antenna technique that can improve the spectral efficiency oftransmissions, thereby significantly boosting the overall data carryingcapacity of wireless systems. The use of multiple-input multiple-output(MIMO) techniques can improve mmWave communications, and has been widelyrecognized a potentially important component for access networksoperating in higher frequencies. MIMO can be used for achievingdiversity gain, spatial multiplexing gain and beamforming gain. Forthese reasons, MIMO systems are an important part of the 3rd and 4thgeneration wireless systems, and are planned for use in 5G systems.

Referring now to FIG. 2 , illustrated is an example schematic systemblock diagram of mobile edge computing platform according to one or moreembodiments. A radio access network intelligent controller (RIC) 202,found within a mobile edge computing (MEC) platform 200, can compriseseveral microservices to increase system efficiencies. For example, themobility as a service (MaaS) function can determine how to treat trafficbased on a mobility state (i.e., moving, non-moving, rate of speed,etc.) of the UE 102. The session management function 206 can maintainsession continuity regardless of where the UE 102 is located within thenetwork. For example, if a user is talking, then the session managementfunction 206 can ensure that the session is not dropped. However, if theuser is checking an email, then session continuity does not need to bemaintained to receive the email. The session IP assignment function 208can be used to maintain session continuity as well. Although a physicalIP address can be changed, the session layer of the IP address cannot bechanged. Thus, the RIC 202 can comprise a microservice that provides thesession IP address assignment. A radio access technology (RAT) IPassignment function 210 is for a physical layer IP that can be used formobility management. If the UE 102 connects to Wi-Fi (e.g., RAT IPAssign Wi-Fi 212) and/or satellite (e.g., RAT IP Assign Sat 214, thenthere can be a corresponding IP address assigned to the UE 102. However,no matter which technology or the mobility status of the UE 102, thepacket data can still be routed (e.g., tunnel-based routing, IPconnection-based routing, etc.) via the routing function 216.

Additionally, the MEC platform 200 can comprise a resource management(RM) function 226 microservice. The RM function 226 can comprise radioresources and transport resources. For example, the transport resourcescan dictate a power change, and/or the power or radio can be turnedon/off in response to a cell being turned on. If the radio is turned on,there can be a transport set-up. Thus, the abstraction of the accessnetwork can know the state of the radio access network (RAN) and whereusers are in relation to radio resources. For instance, in response todata analytics (DA) performed by a DA function 204 for a stadium game,as the game draws closer, the RM function 226 can provide additionalradio resources in anticipation of and/or as additional mobile devicesapproach the stadium.

In yet another embodiment, as a train approaches a train station,additional resources may need to be accessed in accordance withadditional passengers’ mobile devices nearing the train station. The NIB224 can collect RAN and UE resource related information and provide theabstraction of the access network in the real time. ML can predict thereal-time information about the train at time t based on the informationat t-n, t-(n-1), ... t-2, t-1. The RIC 202 can provide the DA on thereal time radio resource needs via the DA function 204. Based on the DAprocured and/or generated by the DA function 204, the RIC 202 caninstruct the network to reduce the overage resources (e.g. power,transport) when the demand is down (e.g. when train leaves the station)and increase the resources when the demand has increased. The resourcescan be increased and/or decreased as a function of time, distance, dataanalytics, predictions, historical values, and/or network load.

The network information base 224 can maintain the state of the RAN(whether the network is congested or not) and the state for each device(e.g., the radio link conditions of each UE 102). A wireless networkdevice 218 operated by the service provider can comprise a policy thatcan determine which microservices should be utilized under certainconditions and in what order (e.g., sequence) the microservices shouldbe executed. The policy can be based on AI/ML and/or operator policies.Within the MEC platform 200, local content 220 can be hosted to improvethe performance, reduce latency, and reduce the transport time. Thewireless network device 218 can receive inputs from the policy function222 to provide guidance on what policies the wireless network device 218should allocate based on certain triggers. The wireless network device218 can have access to the network state, the UE 102 state, and/or aninventory of microservices. There are various network resourcemanagement functions that can address specific aspects of the network(e.g., load balancing functions, handover functions, antenna function,power control functions, etc.). The wireless network device 218 canprovide dynamic allocation of microservices instead of predefineddecisions. Thus, the wireless network device 218 can dynamically outputa policy to the policy function 222, based on the network state, and/orthe UE 102 state and determine which trigger conditions to apply toallocation of microservices and in what order the microservices shouldbe allocated. This data can then be communicated to the RIC 202.

The policy received from the wireless network device 218 can haveintelligence based on AI/ML and can make decisions about whatmicroservices to use and in what order. The policy can also reside onmultiple layers of the system: open network automation process (ONAP),RIC, core, and other areas. The policy from a service level agreement(SLA) can also affect user configuration on their devices. Thus, thedynamic policy can decide which services and what level to be exercised.Machine learning (ML) can reside within the policy and/or at thewireless network device 218. The ML can review the outcomes frompreviously applied policies as a feedback and make a decision at anytime based on network congestion, SLA, premium customers, services withadditional features etc. In an alternative embodiment, the ML can alsobe hosted on the ONAP platform and the ONAP platform can communicatewith the RIC and the policy.

Referring now to FIG. 3 , illustrated is an example schematic systemblock diagram of an example schematic system block diagram of a resourcepreservation framework according to one or more embodiments according toone or more embodiments.

As depicted in FIG. 3 , the wireless network device 218 can comprisesub-components (e.g., resource management component 302, triggeringcomponent 304, AI component 306, and prioritization component 308),processor 310 and memory 312 can bidirectionally communicate with eachother. It should also be noted that in alternative embodiments thatother components including, but not limited to the sub-components,processor 310, and/or memory 312, can be external to the wirelessnetwork device 218. Aspects of the processor 310 can constitutemachine-executable component(s) embodied within machine(s), e.g.,embodied in one or more computer readable mediums (or media) associatedwith one or more machines. Such component(s), when executed by the oneor more machines, e.g., computer(s), computing device(s), virtualmachine(s), etc. can cause the machine(s) to perform the operationsdescribed by the wireless network device 218. In an aspect, the wirelessnetwork device 218 can also include memory 312 that stores computerexecutable components and instructions.

The triggering component 304 can receive/send data associated withtriggers (e.g., time, distance, network load, date, events, etc.) forspecific microservices (e.g., DC function 226, MaaS function, etc.) toaddress specific network-based scenarios. For example, if a network loadexceeds a certain threshold, then that threshold can be the trigger toinvoke a load balancing microservice. Based on dynamic criteria, thetriggering component 304 can trigger additional operations by the MEC200. Consequently, the triggering component 304 can initiate resourceallocation by the resource allocation component 302. The resourceallocation component 302 can pull resources from the network and/orother mobile devices and/or instantiate new resources in response to thetriggering event. Network resources such as bandwidth, network capacity,beam patterns, beam pattern functions, workload assignments, etc., canbe divided between UEs based on a priority associated with the UE inrelation to the triggering event. For example, if the UE 102 isrequesting emergency services and a second mobile device is requestingentertainment services, then the UE 102 can receive the highest priority(based on the state of the UE) via the prioritization component 308because the UE 102 is requesting resources to facilitate mitigation ofan emergency situation.

Priority assignments can be based on the type of UE 102, type ofmicroservice, geographic location, UE 102 power, time, a type ofemergency (e.g., a fire versus a car accident, etc.), number ofconcurrent emergencies, location, etc. Thus, based on the priorityassigned by the prioritization component 308, the network resources canbe allocated to the UE 102, by the resource allocation component 302,accordingly. Additionally, the AI component 306 can learn from previouspatterns associated with microservice coordination, priorities assignedto specific microservices, and/or scenarios and modify microserviceallocation based on the aforementioned factors and/or historicalpatterns analyzed by the AI component 306.

As depicted in FIG. 3 , dynamic radio resources (e.g., power, transport,frequency, digital signal processing, etc.) can be applied to the UEs102 based on ML. For instance, when a train enters a train station, thepersons on the train can need to utilize radio resources. Thus, ML(e.g., facilitated by the AI component 306) and/or network predictionscan provide real-time information, to the MEC 200 from the networkdevice 218, such that radio resources can turned on/off and/or poweredup/down based on a trigger. For example, knowing that a train is at onestation can be an indication that (at another time t) the train will beat another known station. Thus, at time t, resources can be allocatedbased on the predicted utilization by UE 102 a, at time t-1 resourcescan be allocated based on the predicted utilization (e.g., how many UEs102 are on the train, how long will the UEs 102 need services, rate ofspeed of the UEs on the train being the same, etc.) by UEs 102 b, and attime t-2 resources can be allocated based on the predicted utilizationby UEs 102 c. In addition to predicted utilization values, real-timeinformation comprising where the train is, how many UEs 102 are on thetrain, how many UEs 102 are using radio resources, rate of speed of theUEs, whether the UEs are determined to be on a different train thanother UEs, and/or how much the radio resource demand is can be utilizedto determine resource allocation. Based on the real-time information,the radio resources can be actively adjusted. For instance, when thetrain leaves the station, one or more radios can be turned off if theyare unneeded due to people leaving the station. Thus, resources can be afunction of time and/or distance. For example, if it is known that ittakes 360 seconds for a train to arrive at a destination station from acurrent station, and it is known that the train is at the currentstation, as the time decreases from 360 seconds (when the train leavesthe current station), additional resources can be powered up as the 360seconds decreases to 0 seconds (arrival at the destination station).Conversely, as the train leaves the destination station for a newdestination station, currently provided resources can begin to decrease(e.g., by the RM function 226) in accordance with the distance and/ortime out from the destination station. Additionally UEs 102 a, b, c canbe grouped or partitioned based on a rate of speed. For instance, if UEs102 c are all moving at 55 miles per hour in the same direction, it canbe assumed that they are all on the same train headed to the same trainstation.

Referring now to FIG. 4 , illustrated is an example schematic systemblock diagram of flow diagram for a resource preservation frameworkaccording to one or more embodiments. At block 400, data analytics canbe performed to network-based scenarios via the DA function 204. Thedata obtained via the DA function 204 can then be used to generatepredictions based on ML and/or other policies being performed at block402. The ML can generate triggers (e.g., number of UEs approaching theRAN, etc.) based on the DA to determine when/where to allocate networkresources. If the trigger condition is satisfied at block 404, then theRM function 226 can allocate resources to the intended UEs at block 406.However, if the trigger conditions are not satisfied, then the systemcan iteratively continue to perform DA at block 400.

Referring now to FIG. 5 illustrates an example schematic system blockdiagram of a closed loop control system according to one or moreembodiments. A network management platform database 502 can send andreceive data, associated with UEs 102 to block 504 where the UE data canbe collected and/or correlated by a collection and correlationcomponent. For example, location data can be correlated to time dataassociated with a specific UE (e.g., UE 102 is in/near macro-cell at 8ammost mornings). The UE data can comprise UE state data (collection data,correlation data, usage data, device type data, etc.). The UE data canbe sent to the UE data collection and correlation component at block 504from a network conditions and UE measurement component within the RIC202 at block 508. Once the UE data collection and correlation componentreceives the UE data and correlates the UE data, the UE data collectionand correlation component can send the UE data and correlation data to alearning component at block 506. The learning component can utilize AIor machine learning (ML) to detect and/or predict UE mobility andnetwork patterns and modify application of a microservice at block 510.

The network conditions and UE measurement component of centralized units(CUs) and/or distributed units (DUs) at block 512 can send the networkcondition and measurement data to the network conditions and UEmeasurement component within the RIC at block 508. The wireless networkdevice 218 can then use the network measurements to determine whichmicroservices are candidate microservices and then send the candidatemicroservices to the CUs and/or DUs at block 510. Microservices ofneighboring cells of the UE 102 can be received by the CUs and/or DUs atblock 514.

Referring now to FIG. 6 , illustrated is an example flow diagram for amethod for radio access network intelligent controller for a resourcepreservation framework for a 5G network according to one or moreembodiments. At element 600, the method can comprise receiving statedata (e.g., via MEC 200) representative of a state of a group of mobiledevices (e.g., UE 102) as the group of mobile devices approaches thewireless network device (e.g., network node 104). At element 602, themethod can comprise receiving distance data (e.g., via MEC 200)representative of a distance of the group of mobile devices (e.g., UE102) from the network device (e.g., network node 104). Additionally, atelement 604, the method can comprise generating a trigger condition(e.g., DA function 204) to trigger a resource allocation (via the RMfunction 226) of a network resource as a function of the state data andthe distance data. Furthermore, at element 606, the method can comprisemonitoring the state data and the distance data (e.g., DA function 204)of the group of mobile devices (e.g., UE 102) to determine whether thetrigger condition has been satisfied in response to the generating thetrigger condition.

Referring now to FIG. 7 , illustrated is an example flow diagram for asystem for radio access network intelligent controller for a resourcepreservation framework for a 5G network according to one or moreembodiments. At element 700, the system can facilitate, receiving statedata representative of a state of mobile devices (e.g., UE 102) as themobile devices approach a network device (e.g., network node 104) at asame rate of speed. At element 702, the system can comprise receivingdistance data (e.g., DA function 204) representative of a distance ofthe mobile devices (e.g., UE 102) from the network device (e.g., networknode 104). As a function of the state data and the distance data, atelement 704, the system operations can comprise applying a triggercondition to trigger an action associated with applying a microserviceto the mobile devices (e.g., UE 102). Additionally, in response to theapplying the trigger condition (e.g., via machine learning), at element706, the system operations can comprise monitoring the state data andthe distance data (e.g., DA function 204) of the mobile devices (e.g.,UE 102) to determine whether the trigger condition has been satisfied.

Referring now to FIG. 8 , illustrated is an example flow diagram for amachine-readable medium for radio access network intelligent controllerfor a resource preservation framework for a 5G network according to oneor more embodiments. At element 800, the machine-readable medium thatcan perform the operations comprising receiving state data (via the DAfunction 204) representative of a state of mobile devices (e.g., UE 102)as the mobile devices approach a network device (e.g., network node 104)at a same rate of speed. At element 802, the machine-readable medium canperform the operations comprising receiving distance data representative(via the DA function 204) of a distance of the mobile devices (e.g., UE102) from the network device (e.g., network node 104). Additionally, atelement 804, the machine-readable medium can perform the operationscomprising receiving time data representative of a time associated withthe mobile devices (e.g., UE 102) arriving at a location associated withthe network device (e.g., network node 104). Furthermore, at element806, as a function of the state data, the time data, and the distancedata, the machine-readable medium can perform the operations comprisinggenerating a trigger (e.g., via the ML function) condition to trigger anaction associated with applying a microservice to the mobile devices(e.g., UE 102).

Referring now to FIG. 9 , illustrated is a schematic block diagram of anexemplary end-user device such as a mobile device 900 capable ofconnecting to a network in accordance with some embodiments describedherein. Although a mobile handset 900 is illustrated herein, it will beunderstood that other devices can be a mobile device, and that themobile handset 900 is merely illustrated to provide context for theembodiments of the various embodiments described herein. The followingdiscussion is intended to provide a brief, general description of anexample of a suitable environment 900 in which the various embodimentscan be implemented. While the description includes a general context ofcomputer-executable instructions embodied on a machine-readable storagemedium, those skilled in the art will recognize that the innovation alsocan be implemented in combination with other program modules and/or as acombination of hardware and software.

Generally, applications (e.g., program modules) can include routines,programs, components, data structures, etc., that perform particulartasks or implement particular abstract data types. Moreover, thoseskilled in the art will appreciate that the methods described herein canbe practiced with other system configurations, includingsingle-processor or multiprocessor systems, minicomputers, mainframecomputers, as well as personal computers, hand-held computing devices,microprocessor-based or programmable consumer electronics, and the like,each of which can be operatively coupled to one or more associateddevices.

A computing device can typically include a variety of machine-readablemedia. Machine-readable media can be any available media that can beaccessed by the computer and includes both volatile and non-volatilemedia, removable and non-removable media. By way of example and notlimitation, computer-readable media can comprise computer storage mediaand communication media. Computer storage media can include volatileand/or non-volatile media, removable and/or non-removable mediaimplemented in any method or technology for storage of information, suchas computer-readable instructions, data structures, program modules orother data. Computer storage media can include, but is not limited to,RAM, ROM, EEPROM, flash memory or other memory technology, CD ROM,digital video disk (DVD) or other optical disk storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to store thedesired information and which can be accessed by the computer.

Communication media typically embodies computer-readable instructions,data structures, program modules or other data in a modulated datasignal such as a carrier wave or other transport mechanism, and includesany information delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media includes wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared and other wireless media. Combinations of the anyof the above should also be included within the scope ofcomputer-readable media.

The handset 900 includes a processor 902 for controlling and processingall onboard operations and functions. A memory 904 interfaces to theprocessor 902 for storage of data and one or more applications 906(e.g., a video player software, user feedback component software, etc.).Other applications can include voice recognition of predetermined voicecommands that facilitate initiation of the user feedback signals. Theapplications 906 can be stored in the memory 904 and/or in a firmware908, and executed by the processor 902 from either or both the memory904 or/and the firmware 908. The firmware 908 can also store startupcode for execution in initializing the handset 900. A communicationscomponent 910 interfaces to the processor 902 to facilitatewired/wireless communication with external systems, e.g., cellularnetworks, VoIP networks, and so on. Here, the communications component910 can also include a suitable cellular transceiver 911 (e.g., a GSMtransceiver) and/or an unlicensed transceiver 913 (e.g., Wi-Fi, WiMax)for corresponding signal communications. The handset 900 can be a devicesuch as a cellular telephone, a PDA with mobile communicationscapabilities, and messaging-centric devices. The communicationscomponent 910 also facilitates communications reception from terrestrialradio networks (e.g., broadcast), digital satellite radio networks, andInternet-based radio services networks.

The handset 900 includes a display 912 for displaying text, images,video, telephony functions (e.g., a Caller ID function), setupfunctions, and for user input. For example, the display 912 can also bereferred to as a “screen” that can accommodate the presentation ofmultimedia content (e.g., music metadata, messages, wallpaper, graphics,etc.). The display 912 can also display videos and can facilitate thegeneration, editing and sharing of video quotes. A serial I/O interface914 is provided in communication with the processor 902 to facilitatewired and/or wireless serial communications (e.g., USB, and/or IEEE1394) through a hardwire connection, and other serial input devices(e.g., a keyboard, keypad, and mouse). This supports updating andtroubleshooting the handset 900, for example. Audio capabilities areprovided with an audio I/O component 916, which can include a speakerfor the output of audio signals related to, for example, indication thatthe user pressed the proper key or key combination to initiate the userfeedback signal. The audio I/O component 916 also facilitates the inputof audio signals through a microphone to record data and/or telephonyvoice data, and for inputting voice signals for telephone conversations.

The handset 900 can include a slot interface 918 for accommodating a SIC(Subscriber Identity Component) in the form factor of a card SubscriberIdentity Module (SIM) or universal SIM 920, and interfacing the SIM card920 with the processor 902. However, it is to be appreciated that theSIM card 920 can be manufactured into the handset 900, and updated bydownloading data and software.

The handset 900 can process IP data traffic through the communicationcomponent 910 to accommodate IP traffic from an IP network such as, forexample, the Internet, a corporate intranet, a home network, a personarea network, etc., through an ISP or broadband cable provider. Thus,VoIP traffic can be utilized by the handset 900 and IP-based multimediacontent can be received in either an encoded or decoded format.

A video processing component 922 (e.g., a camera) can be provided fordecoding encoded multimedia content. The video processing component 922can aid in facilitating the generation, editing and sharing of videoquotes. The handset 900 also includes a power source 924 in the form ofbatteries and/or an AC power subsystem, which power source 924 caninterface to an external power system or charging equipment (not shown)by a power I/O component 926.

The handset 900 can also include a video component 930 for processingvideo content received and, for recording and transmitting videocontent. For example, the video component 930 can facilitate thegeneration, editing and sharing of video quotes. A location trackingcomponent 932 facilitates geographically locating the handset 900. Asdescribed hereinabove, this can occur when the user initiates thefeedback signal automatically or manually. A user input component 934facilitates the user initiating the quality feedback signal. The userinput component 934 can also facilitate the generation, editing andsharing of video quotes. The user input component 934 can include suchconventional input device technologies such as a keypad, keyboard,mouse, stylus pen, and/or touch screen, for example.

Referring again to the applications 906, a hysteresis component 936facilitates the analysis and processing of hysteresis data, which isutilized to determine when to associate with the access point. Asoftware trigger component 938 can be provided that facilitatestriggering of the hysteresis component 938 when the Wi-Fi transceiver913 detects the beacon of the access point. A SIP client 940 enables thehandset 900 to support SIP protocols and register the subscriber withthe SIP registrar server. The applications 906 can also include a client942 that provides at least the capability of discovery, play and storeof multimedia content, for example, music.

The handset 900, as indicated above related to the communicationscomponent 910, includes an indoor network radio transceiver 913 (e.g.,Wi-Fi transceiver). This function supports the indoor radio link, suchas IEEE 802.11, for the dual-mode GSM handset 900. The handset 900 canaccommodate at least satellite radio services through a handset that cancombine wireless voice and digital radio chipsets into a single handhelddevice.

In order to provide additional context for various embodiments describedherein, FIG. 10 and the following discussion are intended to provide abrief, general description of a suitable computing environment 1000 inwhich the various embodiments of the embodiment described herein can beimplemented. While the embodiments have been described above in thegeneral context of computer-executable instructions that can run on oneor more computers, those skilled in the art will recognize that theembodiments can be also implemented in combination with other programmodules and/or as a combination of hardware and software.

Generally, program modules include routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the disclosed methods can be practiced with other computer systemconfigurations, including single-processor or multiprocessor computersystems, minicomputers, mainframe computers, Internet of Things (IoT)devices, distributed computing systems, as well as personal computers,hand-held computing devices, microprocessor-based or programmableconsumer electronics, and the like, each of which can be operativelycoupled to one or more associated devices.

The illustrated embodiments of the embodiments herein can be alsopracticed in distributed computing environments where certain tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules can be located in both local and remote memory storage devices.

Computing devices typically include a variety of media, which caninclude computer-readable storage media, machine-readable storage media,and/or communications media, which two terms are used herein differentlyfrom one another as follows. Computer-readable storage media ormachine-readable storage media can be any available storage media thatcan be accessed by the computer and includes both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media or machine-readablestorage media can be implemented in connection with any method ortechnology for storage of information such as computer-readable ormachine-readable instructions, program modules, structured data orunstructured data.

Computer-readable storage media can include, but are not limited to,random access memory (RAM), read only memory (ROM), electricallyerasable programmable read only memory (EEPROM), flash memory or othermemory technology, compact disk read only memory (CD-ROM), digitalversatile disk (DVD), Blu-ray disc (BD) or other optical disk storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, solid state drives or other solid statestorage devices, or other tangible and/or non-transitory media which canbe used to store desired information. In this regard, the terms“tangible” or “non-transitory” herein as applied to storage, memory orcomputer-readable media, are to be understood to exclude onlypropagating transitory signals per se as modifiers and do not relinquishrights to all standard storage, memory or computer-readable media thatare not only propagating transitory signals per se.

Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries or otherdata retrieval protocols, for a variety of operations with respect tothe information stored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and includes any information deliveryor transport media. The term “modulated data signal” or signals refersto a signal that has one or more of its characteristics set or changedin such a manner as to encode information in one or more signals. By wayof example, and not limitation, communication media include wired media,such as a wired network or direct-wired connection, and wireless mediasuch as acoustic, RF, infrared and other wireless media.

With reference again to FIG. 10 , the example environment 1000 forimplementing various embodiments of the aspects described hereinincludes a computer 1002, the computer 1002 including a processing unit1004, a system memory 1006 and a system bus 1008. The system bus 1008couples system components including, but not limited to, the systemmemory 1006 to the processing unit 1004. The processing unit 1004 can beany of various commercially available processors. Dual microprocessorsand other multi-processor architectures can also be employed as theprocessing unit 1004.

The system bus 1008 can be any of several types of bus structure thatcan further interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 1006includes ROM 1010 and RAM 1012. A basic input/output system (BIOS) canbe stored in a non-volatile memory such as ROM, erasable programmableread only memory (EPROM), EEPROM, which BIOS contains the basic routinesthat help to transfer information between elements within the computer1002, such as during startup. The RAM 1012 can also include a high-speedRAM such as static RAM for caching data.

The computer 1002 further includes an internal hard disk drive (HDD)1014 (e.g., EIDE, SATA), one or more external storage devices 1016(e.g., a magnetic floppy disk drive (FDD) 1016, a memory stick or flashdrive reader, a memory card reader, etc.) and an optical disk drive 1020(e.g., which can read or write from a CD-ROM disc, a DVD, a BD, etc.).While the internal HDD 1014 is illustrated as located within thecomputer 1002, the internal HDD 1014 can also be configured for externaluse in a suitable chassis (not shown). Additionally, while not shown inenvironment 1000, a solid state drive (SSD) could be used in additionto, or in place of, an HDD 1014. The HDD 1014, external storagedevice(s) 1016 and optical disk drive 1020 can be connected to thesystem bus 1008 by an HDD interface 1024, an external storage interface1026 and an optical drive interface 1028, respectively. The interface1024 for external drive implementations can include at least one or bothof Universal Serial Bus (USB) and Institute of Electrical andElectronics Engineers (IEEE) 1394 interface technologies. Other externaldrive connection technologies are within contemplation of theembodiments described herein.

The drives and their associated computer-readable storage media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 1002, the drives andstorage media accommodate the storage of any data in a suitable digitalformat. Although the description of computer-readable storage mediaabove refers to respective types of storage devices, it should beappreciated by those skilled in the art that other types of storagemedia which are readable by a computer, whether presently existing ordeveloped in the future, could also be used in the example operatingenvironment, and further, that any such storage media can containcomputer-executable instructions for performing the methods describedherein.

A number of program modules can be stored in the drives and RAM 1012,including an operating system 1030, one or more application programs1032, other program modules 1034 and program data 1036. All or portionsof the operating system, applications, modules, and/or data can also becached in the RAM 1012. The systems and methods described herein can beimplemented utilizing various commercially available operating systemsor combinations of operating systems.

Computer 1002 can optionally comprise emulation technologies. Forexample, a hypervisor (not shown) or other intermediary can emulate ahardware environment for operating system 1030, and the emulatedhardware can optionally be different from the hardware illustrated inFIG. 10 . In such an embodiment, operating system 1030 can comprise onevirtual machine (VM) of multiple VMs hosted at computer 1002.Furthermore, operating system 1030 can provide runtime environments,such as the Java runtime environment or the .NET framework, forapplications 1032. Runtime environments are consistent executionenvironments that allow applications 1032 to run on any operating systemthat includes the runtime environment. Similarly, operating system 1030can support containers, and applications 1032 can be in the form ofcontainers, which are lightweight, standalone, executable packages ofsoftware that include, e.g., code, runtime, system tools, systemlibraries and settings for an application.

Further, computer 1002 can be enable with a security module, such as atrusted processing module (TPM). For instance with a TPM, bootcomponents hash next in time boot components, and wait for a match ofresults to secured values, before loading a next boot component. Thisprocess can take place at any layer in the code execution stack ofcomputer 1002, e.g., applied at the application execution level or atthe operating system (OS) kernel level, thereby enabling security at anylevel of code execution.

A user can enter commands and information into the computer 1002 throughone or more wired/wireless input devices, e.g., a keyboard 1038, a touchscreen 1040, and a pointing device, such as a mouse 1042. Other inputdevices (not shown) can include a microphone, an infrared (IR) remotecontrol, a radio frequency (RF) remote control, or other remote control,a joystick, a virtual reality controller and/or virtual reality headset,a game pad, a stylus pen, an image input device, e.g., camera(s), agesture sensor input device, a vision movement sensor input device, anemotion or facial detection device, a biometric input device, e.g.,fingerprint or iris scanner, or the like. These and other input devicesare often connected to the processing unit 1004 through an input deviceinterface 1044 that can be coupled to the system bus 1008, but can beconnected by other interfaces, such as a parallel port, an IEEE 1394serial port, a game port, a USB port, an IR interface, a BLUETOOTH®interface, etc.

A monitor 1046 or other type of display device can be also connected tothe system bus 1008 via an interface, such as a video adapter 1048. Inaddition to the monitor 1046, a computer typically includes otherperipheral output devices (not shown), such as speakers, printers, etc.

The computer 1002 can operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 1050. The remotecomputer(s) 1050 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallyincludes many or all of the elements described relative to the computer1002, although, for purposes of brevity, only a memory/storage device1052 is illustrated. The logical connections depicted includewired/wireless connectivity to a local area network (LAN) 1054 and/orlarger networks, e.g., a wide area network (WAN) 1056. Such LAN and WANnetworking environments are commonplace in offices and companies, andfacilitate enterprise-wide computer networks, such as intranets, all ofwhich can connect to a global communications network, e.g., theInternet.

When used in a LAN networking environment, the computer 1002 can beconnected to the local network 1054 through a wired and/or wirelesscommunication network interface or adapter 1058. The adapter 1058 canfacilitate wired or wireless communication to the LAN 1054, which canalso include a wireless access point (AP) disposed thereon forcommunicating with the adapter 1058 in a wireless mode.

When used in a WAN networking environment, the computer 1002 can includea modem 1060 or can be connected to a communications server on the WAN1056 via other means for establishing communications over the WAN 1056,such as by way of the Internet. The modem 1060, which can be internal orexternal and a wired or wireless device, can be connected to the systembus 1008 via the input device interface 1044. In a networkedenvironment, program modules depicted relative to the computer 1002 orportions thereof, can be stored in the remote memory/storage device1052. It will be appreciated that the network connections shown areexample and other means of establishing a communications link betweenthe computers can be used.

When used in either a LAN or WAN networking environment, the computer1002 can access cloud storage systems or other network-based storagesystems in addition to, or in place of, external storage devices 1016 asdescribed above. Generally, a connection between the computer 1002 and acloud storage system can be established over a LAN 1054 or WAN 1056e.g., by the adapter 1058 or modem 1060, respectively. Upon connectingthe computer 1002 to an associated cloud storage system, the externalstorage interface 1026 can, with the aid of the adapter 1058 and/ormodem 1060, manage storage provided by the cloud storage system as itwould other types of external storage. For instance, the externalstorage interface 1026 can be configured to provide access to cloudstorage sources as if those sources were physically connected to thecomputer 1002.

The computer 1002 can be operable to communicate with any wirelessdevices or entities operatively disposed in wireless communication,e.g., a printer, scanner, desktop and/or portable computer, portabledata assistant, communications satellite, any piece of equipment orlocation associated with a wirelessly detectable tag (e.g., a kiosk,news stand, store shelf, etc.), and telephone. This can include WirelessFidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, thecommunication can be a predefined structure as with a conventionalnetwork or simply an ad hoc communication between at least two devices.

The computer is operable to communicate with any wireless devices orentities operatively disposed in wireless communication, e.g., aprinter, scanner, desktop and/or portable computer, portable dataassistant, communications satellite, any piece of equipment or locationassociated with a wirelessly detectable tag (e.g., a kiosk, news stand,restroom), and telephone. This includes at least Wi-Fi and Bluetooth™wireless technologies. Thus, the communication can be a predefinedstructure as with a conventional network or simply an ad hoccommunication between at least two devices.

Wi-Fi, or Wireless Fidelity, allows connection to the Internet from acouch at home, a bed in a hotel room, or a conference room at work,without wires. Wi-Fi is a wireless technology similar to that used in acell phone that enables such devices, e.g., computers, to send andreceive data indoors and out; anywhere within the range of a basestation. Wi-Fi networks use radio technologies called IEEE 802.11 (a, b,g, etc.) to provide secure, reliable, fast wireless connectivity. AWi-Fi network can be used to connect computers to each other, to theInternet, and to wired networks (which use IEEE 802.3 or Ethernet).Wi-Fi networks operate in the unlicensed 2.4 and 5 GHz radio bands, atan 11 Mbps (802.1 1a) or 54 Mbps (802.1 1b) data rate, for example, orwith products that contain both bands (dual band), so the networks canprovide real-world performance similar to the basic 10BaseT wiredEthernet networks used in many offices.

The above description of illustrated embodiments of the subjectdisclosure, including what is described in the Abstract, is not intendedto be exhaustive or to limit the disclosed embodiments to the preciseforms disclosed. While specific embodiments and examples are describedherein for illustrative purposes, various modifications are possiblethat are considered within the scope of such embodiments and examples,as those skilled in the relevant art can recognize.

In this regard, while the subject matter has been described herein inconnection with various embodiments and corresponding FIGs, whereapplicable, it is to be understood that other similar embodiments can beused or modifications and additions can be made to the describedembodiments for performing the same, similar, alternative, or substitutefunction of the disclosed subject matter without deviating therefrom.Therefore, the disclosed subject matter should not be limited to anysingle embodiment described herein, but rather should be construed inbreadth and scope in accordance with the appended claims below.

What is claimed is:
 1. A method, comprising: analyzing, by a networkdevice comprising a processor, using a machine learning model, a groupof user equipment travelling within a defined area of a network resourceto determine a predicted utilization of the network resource by thegroup of user equipment, wherein the network resource facilitates radionetwork connectivity and is operating according to a first configurationof a resource allocation of the network resource; and initiating, by thenetwork device, using the machine learning model and based on thepredicted utilization of the network resource by the group of userequipment, a second configuration of the resource allocation of thenetwork resource to the group of user equipment, wherein the secondconfiguration of the resource allocation reduces an energy usage of thenetwork resource as compared to the first configuration.
 2. The methodof claim 1, wherein the second configuration modifies a transmit powerof the network resource as compared to the first configuration.
 3. Themethod of claim 1, wherein the second configuration modifies anoperating frequency of the network resource as compared to the firstconfiguration.
 4. The method of claim 1, wherein the secondconfiguration modifies a quantity of active radios of the networkresource as compared to the first configuration.
 5. The method of claim1, wherein the second configuration modifies a beam pattern of thenetwork resource as compared to the first configuration.
 6. The methodof claim 1, wherein the network resource is associated with a trainstation.
 7. The method of claim 1, wherein the network resource isassociated with a stadium.
 8. Network equipment, comprising: aprocessor; and a memory that stores executable instructions that, whenexecuted by the processor, facilitate performance of operations,comprising: determining, using a machine learning model, a predictedutilization of a network resource by a group of user equipment movingwithin a defined area of the network resource, wherein the networkresource provides radio network connectivity and is operating in a firstconfiguration of a resource allocation of the network resource; andinitiating, using the machine learning model, based on the predictedutilization of the network resource by the group of user equipment, asecond configuration of the resource allocation of the network resource,to the group of user equipment, wherein the second configuration of theresource allocation reduces an energy usage of the network resource ascompared to the first configuration.
 9. The network equipment of claim8, wherein the second configuration modifies a transmit power of thenetwork resource as compared to the first configuration.
 10. The networkequipment of claim 8, wherein the second configuration modifies anoperating frequency of the network resource as compared to the firstconfiguration.
 11. The network equipment of claim 8, wherein the secondconfiguration modifies a quantity of active radios of the networkresource as compared to the first configuration.
 12. The networkequipment of claim 8, wherein the second configuration modifies a beampattern of the network resource as compared to the first configuration.13. The network equipment of claim 8, wherein the network resource isassociated with a shopping mall.
 14. The network equipment of claim 8,wherein the network resource is associated with a location in which anemergency is occurring.
 15. A non-transitory machine-readable medium,comprising executable instructions that, when executed by a processor,facilitate performance of operations, comprising: determining, using amachine learning model, a predicted utilization of a network resource bya group of mobile devices moving within a defined area of the networkresource, wherein the network resource enables radio networkconnectivity and is operating according to a first configuration of aresource allocation of the network resource; and based on the predictedutilization of the network resource by the group of mobile devices,activating, using the machine learning model, a second configuration ofthe resource allocation of the network resource to the group of mobiledevices, wherein the second configuration of the resource allocationreduces an energy usage of the network resource as compared to the firstconfiguration.
 16. The non-transitory machine-readable medium of claim15, wherein the second configuration modifies a transmit power of thenetwork resource as compared to the first configuration.
 17. Thenon-transitory machine-readable medium of claim 15, wherein the secondconfiguration modifies an operating frequency of the network resource ascompared to the first configuration.
 18. The non-transitorymachine-readable medium of claim 15, wherein the second configurationmodifies a quantity of active radios of the network resource as comparedto the first configuration.
 19. The non-transitory machine-readablemedium of claim 15, wherein the second configuration modifies a beampattern of the network resource as compared to the first configuration.20. The non-transitory machine-readable medium of claim 15, wherein thenetwork resource is associated with a train station.